Ultrafine,highly dispersed Pt clusters were immobilized onto the Co nanoparticle surfaces by one-step pyrolysis of the precursor Pt(Ⅱ)-encapsulating Co-MOF-74.Owing to the small size effects of Pt clusters as well as...Ultrafine,highly dispersed Pt clusters were immobilized onto the Co nanoparticle surfaces by one-step pyrolysis of the precursor Pt(Ⅱ)-encapsulating Co-MOF-74.Owing to the small size effects of Pt clusters as well as the strongly enhanced synergistic interactions between Pt and Co atoms,the obtained Pt-on-Co/C400 catalysts exhib-ited excellent catalytic activity toward the hydrolysis of ammonia borane with an extremely high turnover frequency(TOF)value of 3022 min^(-1)at 303 K.Durability test indicated that the obtained Pt-on-Co/C400 catalysts possessed high catalytic stability,and there were no changes in the catalyst structures and catalytic activities after 10 cycles.展开更多
An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the...An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the[B_(5)O_(10)]^(5-)and[B_(3)O_(7)]^(5-)clusters are alternately connected to form 1D[B_(8)O_(15)]_(n)^(6n-)chains,which are further linked by AlO_(4)units to form a 2D monolayer with 7‑membered ring and 10‑membered ring windows.Two adjacent monolayers with opposite orientations further form a porous‑layered structure with six channels through B—O—Al bonds.Compound 1 was characterized by single crystal X‑ray diffraction,powder X‑ray diffraction(PXRD),IR spectroscopy,UV‑Vis diffuse reflection spectroscopy,and thermogravimetric analysis(TGA),respectively.UV‑Vis diffuse reflectance analysis indicates that compound 1 shows a wide transparency range with a short cutoff edge of 201 nm,suggesting it may have potential application in UV regions.CCDC:2383923.展开更多
In the paper,we report a highly robust and porous bimetallic Ti-MOF(designated Mg_(2)Ti-ABTC)by utiliz-ing a trinuclear[Mg_(2)TiO(COO)_(6)]cluster and a tetradentate H_(4)ABTC(3,3′,5,5′-azobenzene tetracarboxylic ac...In the paper,we report a highly robust and porous bimetallic Ti-MOF(designated Mg_(2)Ti-ABTC)by utiliz-ing a trinuclear[Mg_(2)TiO(COO)_(6)]cluster and a tetradentate H_(4)ABTC(3,3′,5,5′-azobenzene tetracarboxylic acid)ligand.Mg_(2)Ti-ABTC exhibited permanent porosity for N_(2),CO_(2),CH_(4),C_(2)H_(2),C_(2)H_(4),and C_(2)H_(6)gas adsorption.Further-more,Mg_(2)Ti-ABTC exhibited outstanding photocatalytic activity in the oxidation of aromatic sulfides to the corre-sponding sulfoxides under ambient air conditions.Mechanism studies reveal that photoinduced holes(h^(+)),the super-oxide radical(·O_(2)^(-)),and singlet oxygen(^(1)O_(2))are pivotal species involved in the photocatalytic oxidation reaction.展开更多
When performing tasks,unmanned clusters often face a variety of strategy choices.One of the key issues in unmanned cluster tasks is the method through which to design autonomous collaboration and cooperative evolution...When performing tasks,unmanned clusters often face a variety of strategy choices.One of the key issues in unmanned cluster tasks is the method through which to design autonomous collaboration and cooperative evolution mechanisms that allow for unmanned clusters to maximize their overall task effective-ness under the condition of strategic diversity.This paper ana-lyzes these task requirements from three perspectives:the diver-sity of the decision space,information network construction,and the autonomous collaboration mechanism.Then,this paper pro-poses a method for solving the problem of strategy selection diversity under two network structures.Next,this paper presents a Moran-rule-based evolution dynamics model for unmanned cluster strategies and a vision-driven-mechanism-based evolu-tion dynamics model for unmanned cluster strategy in the con-text of strategy selection diversity according to various unmanned cluster application scenarios.Finally,this paper pro-vides a simulation analysis of the effects of relevant parameters such as the payoff factor and cluster size on cooperative evolu-tion in autonomous cluster collaboration for the two types of models.On this basis,this paper presents advice for effectively addressing diverse choices in unmanned cluster tasks,thereby providing decision support for practical applications of unmanned cluster tasks.展开更多
As battlefield scale enlarges,cross-platform collaborative combat provides an appealing paradigm for modern warfare.Complicated constraints and vast solution space pose great challenge for reasonable and efficient mis...As battlefield scale enlarges,cross-platform collaborative combat provides an appealing paradigm for modern warfare.Complicated constraints and vast solution space pose great challenge for reasonable and efficient mission planning,where path planning and target assignment are tightly coupled.In this paper,we focus on UAV mission planning under carrier delivery mode(e.g.,by aircraft carrier,ground vehicle,or transport aircraft) and design a three-layer hierarchical solution framework.In the first layer,we simultaneously determine delivery points and target set division by clustering.To address the safety concerns of radar risk and UAV endurance,an improved density peak clustering algorithm is developed by constraint fusio n.In the second layer,mission planning within each cluster is viewed as a coope rative multiple-task assignment problem.A hybrid heuristic algorithm that integrates a voting-based heuristic solution generation strategy(VHSG) and a stochastic variable neighborhood search(SVNS),called VHSG-SVNS,is proposed for rapid solution.Based on the results of the first two layers,the third layer transforms carrier path planning into a multiple-vehicle routing problem with time window.The cost between any two nodes is calculated by the A~* algorithm,and the genetic algorithm is then implemented to determine the global route.Finally,a practical mission scenario containing 200 targets is used to validate the effectiveness of the designed framework,where three layers cooperate well with each other to generate satisfactory combat scheduling.Comparisons are made in each layer to highlight optimum-seeking capability and efficiency of the proposed algorithms.Works done in this paper provide a simple but efficient solution framework for cross-platform cooperative mission planning problems,and can be potentially extended to other applications such as post-disaster search and rescue,forest surveillance and firefighting,logistics pick and delivery,etc.展开更多
As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely...As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.展开更多
Unmanned aerial vehicles(UAVs)have become one of the key technologies to achieve future data collection due to their high mobility,rapid deployment,low cost,and the ability to establish line-of-sight communication lin...Unmanned aerial vehicles(UAVs)have become one of the key technologies to achieve future data collection due to their high mobility,rapid deployment,low cost,and the ability to establish line-of-sight communication links.However,when UAV swarm perform tasks in narrow spaces,they often encounter various spatial obstacles,building shielding materials,and high-speed node movements,which result in intermittent network communication links and cannot support the smooth comple-tion of tasks.In this paper,a high mobility and dynamic topol-ogy of the UAV swarm is particularly considered and the high dynamic mobile topology-based clustering(HDMTC)algorithm is proposed.Simulation and real flight verification results verify that the proposed HDMTC algorithm achieves higher stability of net-work,longer link expiration time(LET),and longer node lifetime,all of which improve the communication performance for UAV swarm networks.展开更多
针对点云数据中噪声点的剔除问题,提出了一种基于改进DBSCAN(density-based spatial clustering of applications with noise)算法的多尺度点云去噪方法。应用统计滤波对孤立离群点进行预筛选,去除点云中的大尺度噪声;对DBSCAN算法进行...针对点云数据中噪声点的剔除问题,提出了一种基于改进DBSCAN(density-based spatial clustering of applications with noise)算法的多尺度点云去噪方法。应用统计滤波对孤立离群点进行预筛选,去除点云中的大尺度噪声;对DBSCAN算法进行优化,减少算法时间复杂度和实现参数的自适应调整,以此将点云分为正常簇、疑似簇及异常簇,并立即去除异常簇;利用距离共识评估法对疑似簇进行精细判定,通过计算疑似点与其最近的正常点拟合表面之间的距离,判定其是否为异常,有效保持了数据的关键特征和模型敏感度。利用该方法对两个船体分段点云进行去噪,并与其他去噪算法进行对比,结果表明,该方法在去噪效率和特征保持方面具有优势,精确地保留了点云数据的几何特性。展开更多
Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for t...Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.展开更多
Aiming at the triangular fuzzy(TF)multi-attribute decision making(MADM)problem with a preference for the distribution density of attribute(DDA),a decision making method with TF number two-dimensional density(TFTD)oper...Aiming at the triangular fuzzy(TF)multi-attribute decision making(MADM)problem with a preference for the distribution density of attribute(DDA),a decision making method with TF number two-dimensional density(TFTD)operator is proposed based on the density operator theory for the decision maker(DM).Firstly,a simple TF vector clustering method is proposed,which considers the feature of TF number and the geometric distance of vectors.Secondly,the least deviation sum of squares method is used in the program model to obtain the density weight vector.Then,two TFTD operators are defined,and the MADM method based on the TFTD operator is proposed.Finally,a numerical example is given to illustrate the superiority of this method,which can not only solve the TF MADM problem with a preference for the DDA but also help the DM make an overall comparison.展开更多
Background As the most widely cultivated fiber crop,cotton production depends on hybridization to unlock the yield potential of current varieties.A deep understanding of genetic dissection is crucial for the cultivati...Background As the most widely cultivated fiber crop,cotton production depends on hybridization to unlock the yield potential of current varieties.A deep understanding of genetic dissection is crucial for the cultivation of enhanced hybrid plants with desired traits,such as high yield and fine fiber quality.In this study,the general combining ability(GCA)and specific combining ability(SCA)of yield and fiber quality of nine cotton parents(six lines and three testers)and eighteen F1 crosses produced using a line×tester mating design were analyzed.Results The results revealed significant effects of genotypes,parents,crosses,and interactions between parents and crosses for most of the studied traits.Moreover,the effects of both additive and non-additive gene actions played a notably significant role in the inheritance of most of the yield and fiber quality attributes.The F1 hybrids of(Giza 90×Aust)×Giza 86,Uzbekistan 1×Giza 97,and Giza 96×Giza 97 demonstrated superior performance due to their favorable integration of high yield attributes and premium fiber quality characteristics.Path analysis revealed that lint yield has the highest positive direct effect on seed cotton yield,while lint percentage showed the highest negative direct effect on seed cotton yield.Principal component analysis identified specific parents and hybrids associated with higher cotton yield,fiber quality,and other agronomic traits.Conclusion This study provides insights into identifying potential single-and three-way cross hybrids with superior cotton yield and fiber quality characteristics,laying a foundation for future research on improving fiber quality in cotton.展开更多
To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location al...To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines.After establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions.Building on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City.The results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.展开更多
Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition me...Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.展开更多
The observation error model of the underwater acous-tic positioning system is an important factor to influence the positioning accuracy of the underwater target.For the position inconsistency error caused by consideri...The observation error model of the underwater acous-tic positioning system is an important factor to influence the positioning accuracy of the underwater target.For the position inconsistency error caused by considering the underwater tar-get as a mass point,as well as the observation system error,the traditional error model best estimation trajectory(EMBET)with little observed data and too many parameters can lead to the ill-condition of the parameter model.In this paper,a multi-station fusion system error model based on the optimal polynomial con-straint is constructed,and the corresponding observation sys-tem error identification based on improved spectral clustering is designed.Firstly,the reduced parameter unified modeling for the underwater target position parameters and the system error is achieved through the polynomial optimization.Then a multi-sta-tion non-oriented graph network is established,which can address the problem of the inaccurate identification for the sys-tem errors.Moreover,the similarity matrix of the spectral cluster-ing is improved,and the iterative identification for the system errors based on the improved spectral clustering is proposed.Finally,the comprehensive measured data of long baseline lake test and sea test show that the proposed method can accu-rately identify the system errors,and moreover can improve the positioning accuracy for the underwater target positioning.展开更多
Electrocatalytic water splitting provides an efficient method for the production of hydrogen.In electrocatalytic water splitting,the oxygen evolution reaction(OER)involves a kinetically sluggish four-electron transfer...Electrocatalytic water splitting provides an efficient method for the production of hydrogen.In electrocatalytic water splitting,the oxygen evolution reaction(OER)involves a kinetically sluggish four-electron transfer process,which limits the efficiency of electrocatalytic water splitting.Therefore,it is urgent to develop highly active OER catalysts to accelerate reaction kinetics.Coupling single atoms and clusters in one system is an innovative approach for developing efficient catalysts that can synergistically optimize the adsorption and configuration of intermediates and improve catalytic activity.However,research in this area is still scarce.Herein,we constructed a heterogeneous single-atom cluster system by anchoring Ir single atoms and Co clusters on the surface of Ni(OH)_(2)nanosheets.Ir single atoms and Co clusters synergistically improved the catalytic activity toward the OER.Specifically,Co_(n)Ir_(1)/Ni(OH)_(2)required an overpotential of 255 mV at a current density of 10 mA·cm^(−2),which was 60 mV and 67 mV lower than those of Co_(n)/Ni(OH)_(2)and Ir1/Ni(OH)_(2),respectively.The turnover frequency of Co_(n)Ir_(1)/Ni(OH)_(2)was 0.49 s^(−1),which was 4.9 times greater than that of Co_(n)/Ni(OH)_(2)at an overpotential of 300 mV.展开更多
Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface ...Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.展开更多
The advancement of Clustered Regularly Interspaced Short Palindromic Repeats(CRISPR)gene editing technology has revolutionized the comprehension of human genome,propelling molecular and cellular biology research into ...The advancement of Clustered Regularly Interspaced Short Palindromic Repeats(CRISPR)gene editing technology has revolutionized the comprehension of human genome,propelling molecular and cellular biology research into unexplored realms and accelerating progress in life sciences and medicine.CRISPR-based gene screening,recognized for its efficiency and practicality,is widely utilized across diverse biological fields.Aging is a multifaceted process governed by a myriad of genetic and epigenetic factors.Unraveling the genes regulating aging holds promise for understanding this intricate phenomenon and devising strategies for its assessment and intervention.This review provides a comprehensive overview of the progress in CRISPR screening and its applications in aging research,while also offering insights into future directions.CRISPR-based genetic-manipulation tools are positioned as indispensable instruments for mitigating aging and managing age-related diseases.展开更多
文摘Ultrafine,highly dispersed Pt clusters were immobilized onto the Co nanoparticle surfaces by one-step pyrolysis of the precursor Pt(Ⅱ)-encapsulating Co-MOF-74.Owing to the small size effects of Pt clusters as well as the strongly enhanced synergistic interactions between Pt and Co atoms,the obtained Pt-on-Co/C400 catalysts exhib-ited excellent catalytic activity toward the hydrolysis of ammonia borane with an extremely high turnover frequency(TOF)value of 3022 min^(-1)at 303 K.Durability test indicated that the obtained Pt-on-Co/C400 catalysts possessed high catalytic stability,and there were no changes in the catalyst structures and catalytic activities after 10 cycles.
文摘An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the[B_(5)O_(10)]^(5-)and[B_(3)O_(7)]^(5-)clusters are alternately connected to form 1D[B_(8)O_(15)]_(n)^(6n-)chains,which are further linked by AlO_(4)units to form a 2D monolayer with 7‑membered ring and 10‑membered ring windows.Two adjacent monolayers with opposite orientations further form a porous‑layered structure with six channels through B—O—Al bonds.Compound 1 was characterized by single crystal X‑ray diffraction,powder X‑ray diffraction(PXRD),IR spectroscopy,UV‑Vis diffuse reflection spectroscopy,and thermogravimetric analysis(TGA),respectively.UV‑Vis diffuse reflectance analysis indicates that compound 1 shows a wide transparency range with a short cutoff edge of 201 nm,suggesting it may have potential application in UV regions.CCDC:2383923.
文摘In the paper,we report a highly robust and porous bimetallic Ti-MOF(designated Mg_(2)Ti-ABTC)by utiliz-ing a trinuclear[Mg_(2)TiO(COO)_(6)]cluster and a tetradentate H_(4)ABTC(3,3′,5,5′-azobenzene tetracarboxylic acid)ligand.Mg_(2)Ti-ABTC exhibited permanent porosity for N_(2),CO_(2),CH_(4),C_(2)H_(2),C_(2)H_(4),and C_(2)H_(6)gas adsorption.Further-more,Mg_(2)Ti-ABTC exhibited outstanding photocatalytic activity in the oxidation of aromatic sulfides to the corre-sponding sulfoxides under ambient air conditions.Mechanism studies reveal that photoinduced holes(h^(+)),the super-oxide radical(·O_(2)^(-)),and singlet oxygen(^(1)O_(2))are pivotal species involved in the photocatalytic oxidation reaction.
基金supported by the National Natural Science Foundation of China(72471240).
文摘When performing tasks,unmanned clusters often face a variety of strategy choices.One of the key issues in unmanned cluster tasks is the method through which to design autonomous collaboration and cooperative evolution mechanisms that allow for unmanned clusters to maximize their overall task effective-ness under the condition of strategic diversity.This paper ana-lyzes these task requirements from three perspectives:the diver-sity of the decision space,information network construction,and the autonomous collaboration mechanism.Then,this paper pro-poses a method for solving the problem of strategy selection diversity under two network structures.Next,this paper presents a Moran-rule-based evolution dynamics model for unmanned cluster strategies and a vision-driven-mechanism-based evolu-tion dynamics model for unmanned cluster strategy in the con-text of strategy selection diversity according to various unmanned cluster application scenarios.Finally,this paper pro-vides a simulation analysis of the effects of relevant parameters such as the payoff factor and cluster size on cooperative evolu-tion in autonomous cluster collaboration for the two types of models.On this basis,this paper presents advice for effectively addressing diverse choices in unmanned cluster tasks,thereby providing decision support for practical applications of unmanned cluster tasks.
文摘As battlefield scale enlarges,cross-platform collaborative combat provides an appealing paradigm for modern warfare.Complicated constraints and vast solution space pose great challenge for reasonable and efficient mission planning,where path planning and target assignment are tightly coupled.In this paper,we focus on UAV mission planning under carrier delivery mode(e.g.,by aircraft carrier,ground vehicle,or transport aircraft) and design a three-layer hierarchical solution framework.In the first layer,we simultaneously determine delivery points and target set division by clustering.To address the safety concerns of radar risk and UAV endurance,an improved density peak clustering algorithm is developed by constraint fusio n.In the second layer,mission planning within each cluster is viewed as a coope rative multiple-task assignment problem.A hybrid heuristic algorithm that integrates a voting-based heuristic solution generation strategy(VHSG) and a stochastic variable neighborhood search(SVNS),called VHSG-SVNS,is proposed for rapid solution.Based on the results of the first two layers,the third layer transforms carrier path planning into a multiple-vehicle routing problem with time window.The cost between any two nodes is calculated by the A~* algorithm,and the genetic algorithm is then implemented to determine the global route.Finally,a practical mission scenario containing 200 targets is used to validate the effectiveness of the designed framework,where three layers cooperate well with each other to generate satisfactory combat scheduling.Comparisons are made in each layer to highlight optimum-seeking capability and efficiency of the proposed algorithms.Works done in this paper provide a simple but efficient solution framework for cross-platform cooperative mission planning problems,and can be potentially extended to other applications such as post-disaster search and rescue,forest surveillance and firefighting,logistics pick and delivery,etc.
基金supported by the National Natural Science Foundation of China(62375013).
文摘As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.
基金supported by the National Key Research and Development Program of China(2024YFB4504500)Shanghai Collaborative Innovation Project(24xtcx00500).
文摘Unmanned aerial vehicles(UAVs)have become one of the key technologies to achieve future data collection due to their high mobility,rapid deployment,low cost,and the ability to establish line-of-sight communication links.However,when UAV swarm perform tasks in narrow spaces,they often encounter various spatial obstacles,building shielding materials,and high-speed node movements,which result in intermittent network communication links and cannot support the smooth comple-tion of tasks.In this paper,a high mobility and dynamic topol-ogy of the UAV swarm is particularly considered and the high dynamic mobile topology-based clustering(HDMTC)algorithm is proposed.Simulation and real flight verification results verify that the proposed HDMTC algorithm achieves higher stability of net-work,longer link expiration time(LET),and longer node lifetime,all of which improve the communication performance for UAV swarm networks.
文摘针对点云数据中噪声点的剔除问题,提出了一种基于改进DBSCAN(density-based spatial clustering of applications with noise)算法的多尺度点云去噪方法。应用统计滤波对孤立离群点进行预筛选,去除点云中的大尺度噪声;对DBSCAN算法进行优化,减少算法时间复杂度和实现参数的自适应调整,以此将点云分为正常簇、疑似簇及异常簇,并立即去除异常簇;利用距离共识评估法对疑似簇进行精细判定,通过计算疑似点与其最近的正常点拟合表面之间的距离,判定其是否为异常,有效保持了数据的关键特征和模型敏感度。利用该方法对两个船体分段点云进行去噪,并与其他去噪算法进行对比,结果表明,该方法在去噪效率和特征保持方面具有优势,精确地保留了点云数据的几何特性。
基金the National Natural Science Foundation of China(Grant No.62101579).
文摘Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.
基金supported by the Natural Science Foundation of Hunan Province(2023JJ50047,2023JJ40306)the Research Foundation of Education Bureau of Hunan Province(23A0494,20B260)the Key R&D Projects of Hunan Province(2019SK2331)。
文摘Aiming at the triangular fuzzy(TF)multi-attribute decision making(MADM)problem with a preference for the distribution density of attribute(DDA),a decision making method with TF number two-dimensional density(TFTD)operator is proposed based on the density operator theory for the decision maker(DM).Firstly,a simple TF vector clustering method is proposed,which considers the feature of TF number and the geometric distance of vectors.Secondly,the least deviation sum of squares method is used in the program model to obtain the density weight vector.Then,two TFTD operators are defined,and the MADM method based on the TFTD operator is proposed.Finally,a numerical example is given to illustrate the superiority of this method,which can not only solve the TF MADM problem with a preference for the DDA but also help the DM make an overall comparison.
文摘Background As the most widely cultivated fiber crop,cotton production depends on hybridization to unlock the yield potential of current varieties.A deep understanding of genetic dissection is crucial for the cultivation of enhanced hybrid plants with desired traits,such as high yield and fine fiber quality.In this study,the general combining ability(GCA)and specific combining ability(SCA)of yield and fiber quality of nine cotton parents(six lines and three testers)and eighteen F1 crosses produced using a line×tester mating design were analyzed.Results The results revealed significant effects of genotypes,parents,crosses,and interactions between parents and crosses for most of the studied traits.Moreover,the effects of both additive and non-additive gene actions played a notably significant role in the inheritance of most of the yield and fiber quality attributes.The F1 hybrids of(Giza 90×Aust)×Giza 86,Uzbekistan 1×Giza 97,and Giza 96×Giza 97 demonstrated superior performance due to their favorable integration of high yield attributes and premium fiber quality characteristics.Path analysis revealed that lint yield has the highest positive direct effect on seed cotton yield,while lint percentage showed the highest negative direct effect on seed cotton yield.Principal component analysis identified specific parents and hybrids associated with higher cotton yield,fiber quality,and other agronomic traits.Conclusion This study provides insights into identifying potential single-and three-way cross hybrids with superior cotton yield and fiber quality characteristics,laying a foundation for future research on improving fiber quality in cotton.
基金National Natural Science Foundation of China(62202477)。
文摘To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines.After establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions.Building on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City.The results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.
基金supported by the National Natural Science Foundation of China (Project No.72301293)。
文摘Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.
基金This work was supported by the National Natural Science Foundation of China(61903086,61903366,62001115)the Natural Science Foundation of Hunan Province(2019JJ50745,2020JJ4280,2021JJ40133)the Fundamentals and Basic of Applications Research Foundation of Guangdong Province(2019A1515110136).
文摘The observation error model of the underwater acous-tic positioning system is an important factor to influence the positioning accuracy of the underwater target.For the position inconsistency error caused by considering the underwater tar-get as a mass point,as well as the observation system error,the traditional error model best estimation trajectory(EMBET)with little observed data and too many parameters can lead to the ill-condition of the parameter model.In this paper,a multi-station fusion system error model based on the optimal polynomial con-straint is constructed,and the corresponding observation sys-tem error identification based on improved spectral clustering is designed.Firstly,the reduced parameter unified modeling for the underwater target position parameters and the system error is achieved through the polynomial optimization.Then a multi-sta-tion non-oriented graph network is established,which can address the problem of the inaccurate identification for the sys-tem errors.Moreover,the similarity matrix of the spectral cluster-ing is improved,and the iterative identification for the system errors based on the improved spectral clustering is proposed.Finally,the comprehensive measured data of long baseline lake test and sea test show that the proposed method can accu-rately identify the system errors,and moreover can improve the positioning accuracy for the underwater target positioning.
基金supported by the National Key Research and Development Program of China(2021YFA1500500,2019-YFA0405600)the CAS Project for Young Scientists in Basic Research(YSBR-051)+6 种基金the National Science Fund for Distinguished Young Scholars(21925204)the National Natural Science Foundation of China(22202192,U19A2015,22221003,22250007,22163002)the Collaborative Innovation Program of Hefei Science Center,CAS(2022HSCCIP004)the International Partnership,the DNL Cooperation Fund,CAS(DNL202003)the USTC Research Funds of the Double First-Class Initiative(YD9990002016,YD999000-2014)the Program of Chinese Academy of Sciences(123GJHZ2022101GC)the Fundamental Research Funds for the Central Universities(WK9990000095,WK999000-0124).
文摘Electrocatalytic water splitting provides an efficient method for the production of hydrogen.In electrocatalytic water splitting,the oxygen evolution reaction(OER)involves a kinetically sluggish four-electron transfer process,which limits the efficiency of electrocatalytic water splitting.Therefore,it is urgent to develop highly active OER catalysts to accelerate reaction kinetics.Coupling single atoms and clusters in one system is an innovative approach for developing efficient catalysts that can synergistically optimize the adsorption and configuration of intermediates and improve catalytic activity.However,research in this area is still scarce.Herein,we constructed a heterogeneous single-atom cluster system by anchoring Ir single atoms and Co clusters on the surface of Ni(OH)_(2)nanosheets.Ir single atoms and Co clusters synergistically improved the catalytic activity toward the OER.Specifically,Co_(n)Ir_(1)/Ni(OH)_(2)required an overpotential of 255 mV at a current density of 10 mA·cm^(−2),which was 60 mV and 67 mV lower than those of Co_(n)/Ni(OH)_(2)and Ir1/Ni(OH)_(2),respectively.The turnover frequency of Co_(n)Ir_(1)/Ni(OH)_(2)was 0.49 s^(−1),which was 4.9 times greater than that of Co_(n)/Ni(OH)_(2)at an overpotential of 300 mV.
基金supported by the Future Challenge Program through the Agency for Defense Development funded by the Defense Acquisition Program Administration (No.UC200015RD)。
文摘Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.
文摘The advancement of Clustered Regularly Interspaced Short Palindromic Repeats(CRISPR)gene editing technology has revolutionized the comprehension of human genome,propelling molecular and cellular biology research into unexplored realms and accelerating progress in life sciences and medicine.CRISPR-based gene screening,recognized for its efficiency and practicality,is widely utilized across diverse biological fields.Aging is a multifaceted process governed by a myriad of genetic and epigenetic factors.Unraveling the genes regulating aging holds promise for understanding this intricate phenomenon and devising strategies for its assessment and intervention.This review provides a comprehensive overview of the progress in CRISPR screening and its applications in aging research,while also offering insights into future directions.CRISPR-based genetic-manipulation tools are positioned as indispensable instruments for mitigating aging and managing age-related diseases.
